An Efficient AI and IoT Enabled System for Human Activity Monitoring and Fall Detection

Abstract

Falls present a significant health risk, particularly among the elderly, necessitating reliable wearable fall detection systems. This paper introduces an advanced AI-powered system that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation and Convolutional Neural Networks (CNNs) for robust fall detection and daily activity recognition. The primary challenge in developing effective fall detection systems lies in the scarcity and diversity of real-world fall data. This paper addresses this challenge innovatively by employing a GAN trained on datasets of authentic fall events to generate synthetic data. This augmentation strategy significantly expands the training dataset, enhancing the model's capacity to generalize across various fall scenarios and daily activities. The system leverages a specialized 1D CNN architecture designed for processing accelerometer and gyroscope readings obtained from wearable devices, enabling precise feature extraction to distinguish subtle differences between falls and routine movements. The evaluation results demonstrate a notable advancement by achieving a superior accuracy of 99 % for fall detection while minimizing false positives. The developed CNN model can also classify 15 kinds of falls and 19 types of daily life activities. © 2024 IEEE.